Affiliation:
1. Department of Entomology and Plant Pathology, University of Tennessee, USA
2. Department of Horticultural Science, NC State University, USA
Abstract
Abstract
Next-generation sequencing (NGS) enables massively parallel acquisition of large-scale omics data; however, objective data quality filtering parameters are lacking. Although a useful metric, evidence reveals that platform-generated Phred values overestimate per-base quality scores. We have developed novel and empirically based algorithms that streamline NGS data quality filtering. The pipeline leverages known sequence motifs to enable empirical estimation of error rates, detection of erroneous base calls and removal of contaminating adapter sequence. The performance of motif-based error detection and quality filtering were further validated with read compression rates as an unbiased metric. Elevated error rates at read ends, where known motifs lie, tracked with propagation of erroneous base calls. Barcode swapping, an inherent problem with pooled libraries, was also effectively mitigated. The ngsComposer pipeline is suitable for various NGS protocols and platforms due to the universal concepts on which the algorithms are based.
Funder
USDA-NIFA Hatch
Bill and Melinda Gates Foundation
Illumina Agricultural Greater Good Initiative
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
8 articles.
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